Query & integrate data#
import lamindb as ln
import lnschema_bionty as lb
lb.settings.organism = "human"
馃挕 lamindb instance: testuser1/test-facs
ln.track()
馃挕 notebook imports: lamindb==0.63.2 lnschema_bionty==0.35.3
馃挕 saved: Transform(uid='wukchS8V976Uz8', name='Query & integrate data', short_name='facs3', version='0', type=notebook, updated_at=2023-12-05 17:31:03 UTC, created_by_id=1)
馃挕 saved: Run(uid='LGId74tUVX2ff3iFyu4o', run_at=2023-12-05 17:31:03 UTC, transform_id=3, created_by_id=1)
Inspect the CellMarker registry 
#
Inspect your aggregated cell marker registry as a DataFrame:
lb.CellMarker.filter().df().head()
| uid | name | synonyms | gene_symbol | ncbi_gene_id | uniprotkb_id | organism_id | bionty_source_id | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 1 | Nb2sscq9cBcB | CD57 | B3GAT1 | 27087 | Q9P2W7 | 1 | 18.0 | 2023-12-05 17:30:43.249707+00:00 | 1 | |
| 2 | 8OhpfB7wwV32 | Cd19 | CD19 | 930 | P15391 | 1 | 18.0 | 2023-12-05 17:30:43.249747+00:00 | 1 | |
| 3 | HEK41hvaIazP | Cd4 | CD4 | 920 | B4DT49 | 1 | 18.0 | 2023-12-05 17:30:43.249781+00:00 | 1 | |
| 4 | ttBc0Fs01sYk | CD8 | CD8A | 925 | P01732 | 1 | 18.0 | 2023-12-05 17:30:43.249813+00:00 | 1 | |
| 5 | 0evamYEdmaoY | Igd | None | None | None | 1 | 18.0 | 2023-12-05 17:30:43.249846+00:00 | 1 |
Search for a marker (synonyms aware):
lb.CellMarker.search("PD-1").head(2)
| uid | synonyms | score | |
|---|---|---|---|
| name | |||
| PD1 | 2VeZenLi2dj5 | PID1|PD-1|PD 1 | 100.0 |
| CD14/19 | 9VptKqpwq9BZ | 54.5 |
Look up markers with auto-complete:
markers = lb.CellMarker.lookup()
markers.cd8
CellMarker(uid='ttBc0Fs01sYk', name='CD8', synonyms='', gene_symbol='CD8A', ncbi_gene_id='925', uniprotkb_id='P01732', updated_at=2023-12-05 17:30:43 UTC, organism_id=1, bionty_source_id=18, created_by_id=1)
Query files by markers #
Query panels and datasets based on markers, e.g., which datasets have 'CD8' in the flow panel:
panels_with_cd8 = ln.FeatureSet.filter(cell_markers=markers.cd8).all()
ln.File.filter(feature_sets__in=panels_with_cd8).df()
| uid | storage_id | key | suffix | accessor | description | version | size | hash | hash_type | transform_id | run_id | initial_version_id | visibility | key_is_virtual | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||
| 1 | 2CyRKYNKo9riUYMJVoRB | 1 | None | .h5ad | AnnData | Alpert19 | None | 33369696 | VsTnnzHN63ovNESaJtlRUQ | md5 | 1 | 1 | None | 1 | True | 2023-12-05 17:30:47.946139+00:00 | 1 |
| 2 | DAEqbNujr4pFDAl6utxB | 1 | None | .h5ad | AnnData | Oetjen18_t1 | None | 46501304 | I8nRS02iBs5z1J01b2qwOg | md5 | 2 | 2 | None | 1 | True | 2023-12-05 17:30:57.426334+00:00 | 1 |
Access registries:
features = ln.Feature.lookup()
Find shared cell markers between two files:
files = ln.File.filter(feature_sets__in=panels_with_cd8).list()
file1, file2 = files[0], files[1]
shared_markers = file1.features["var"] & file2.features["var"]
shared_markers.list("name")
['Cd4', 'CD8', 'CD3', 'CD27', 'Ccr7', 'CD45RA']